H1N1_FinalAnalysis.ipynb

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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "      <th>Link</th>\n",
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       "      <th>0</th>\n",
       "      <td>2009-04-24</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.who.int/csr/don/2009_04_24/en/</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-24</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.who.int/csr/don/2009_04_24/en/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-04-26</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.who.int/csr/don/2009_04_24/en/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-04-26</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.who.int/csr/don/2009_04_24/en/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-04-27</td>\n",
       "      <td>Canada</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.who.int/csr/don/2009_04_24/en/</td>\n",
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      "text/plain": [
       "         Date                   Country  Cumulative no. of cases  \\\n",
       "0  2009-04-24                    Mexico                       18   \n",
       "1  2009-04-24  United States of America                        7   \n",
       "2  2009-04-26                    Mexico                       18   \n",
       "3  2009-04-26  United States of America                       20   \n",
       "4  2009-04-27                    Canada                        6   \n",
       "\n",
       "   Cumulative no. of deaths                                        Link  \n",
       "0                         0  https://www.who.int/csr/don/2009_04_24/en/  \n",
       "1                         0  https://www.who.int/csr/don/2009_04_24/en/  \n",
       "2                         0  https://www.who.int/csr/don/2009_04_24/en/  \n",
       "3                         0  https://www.who.int/csr/don/2009_04_24/en/  \n",
       "4                         0  https://www.who.int/csr/don/2009_04_24/en/  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import linregress, pearsonr\n",
    "\n",
    "import requests\n",
    "import gmaps\n",
    "from pprint import pprint\n",
    "from scipy.stats import boxcox\n",
    "\n",
    "from config import gkey\n",
    "\n",
    "# Configure gmaps\n",
    "gmaps.configure(api_key=gkey)\n",
    "\n",
    "data = pd.read_csv(\"Resources/H1N1_dataset.csv\")\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
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       "      <td>2009-04-26</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-04-26</td>\n",
       "      <td>United States of America</td>\n",
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       "      <th>4</th>\n",
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       "      <th>2485</th>\n",
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       "      <td>206</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2490 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date                   Country  Cumulative no. of cases  \\\n",
       "0     2009-04-24                    Mexico                       18   \n",
       "1     2009-04-24  United States of America                        7   \n",
       "2     2009-04-26                    Mexico                       18   \n",
       "3     2009-04-26  United States of America                       20   \n",
       "4     2009-04-27                    Canada                        6   \n",
       "...          ...                       ...                      ...   \n",
       "2485  2009-07-06                 Venezuela                      206   \n",
       "2486  2009-07-06                  Viet Nam                      181   \n",
       "2487  2009-07-06            Virgin Islands                        1   \n",
       "2488  2009-07-06  West Bank and Gaza Strip                       60   \n",
       "2489  2009-07-06                     Yemen                        8   \n",
       "\n",
       "      Cumulative no. of deaths  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  \n",
       "...                        ...  \n",
       "2485                         0  \n",
       "2486                         0  \n",
       "2487                         0  \n",
       "2488                         0  \n",
       "2489                         0  \n",
       "\n",
       "[2490 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_df = data[[\"Date\", \"Country\", \"Cumulative no. of cases\", \"Cumulative no. of deaths\"]]\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "len(data_df.Country.unique())"
   ]
  },
  {
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   "execution_count": 4,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "      <td>Viet Nam</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2488</th>\n",
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       "      <td>2009-07-06</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>8</td>\n",
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       "<p>2490 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "            Date                   Country  Total Cases  Total Deaths\n",
       "0     2009-04-24                    Mexico           18             0\n",
       "1     2009-04-24  United States of America            7             0\n",
       "2     2009-04-26                    Mexico           18             0\n",
       "3     2009-04-26  United States of America           20             0\n",
       "4     2009-04-27                    Canada            6             0\n",
       "...          ...                       ...          ...           ...\n",
       "2485  2009-07-06                 Venezuela          206             0\n",
       "2486  2009-07-06                  Viet Nam          181             0\n",
       "2487  2009-07-06            Virgin Islands            1             0\n",
       "2488  2009-07-06  West Bank and Gaza Strip           60             0\n",
       "2489  2009-07-06                     Yemen            8             0\n",
       "\n",
       "[2490 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = data_df.rename(columns={\"Cumulative no. of cases\": \"Total Cases\", \"Cumulative no. of deaths\": \"Total Deaths\"})\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
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       "      <th>Country</th>\n",
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       "      <th>Algeria</th>\n",
       "      <td>2009-06-22</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>2009-06-24</td>\n",
       "      <td>2009-07-06</td>\n",
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       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2009-05-09</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2009-05-09</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>2009-04-29</td>\n",
       "      <td>2009-07-06</td>\n",
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       "      <th>...</th>\n",
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       "      <th>Venezuela</th>\n",
       "      <td>2009-06-01</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Viet Nam</th>\n",
       "      <td>2009-06-01</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank and Gaza Strip</th>\n",
       "      <td>2009-06-15</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>2009-06-17</td>\n",
       "      <td>2009-07-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               first        last\n",
       "Country                                         \n",
       "Algeria                   2009-06-22  2009-07-06\n",
       "Antigua and Barbuda       2009-06-24  2009-07-06\n",
       "Argentina                 2009-05-09  2009-07-06\n",
       "Australia                 2009-05-09  2009-07-06\n",
       "Austria                   2009-04-29  2009-07-06\n",
       "...                              ...         ...\n",
       "Venezuela                 2009-06-01  2009-07-06\n",
       "Viet Nam                  2009-06-01  2009-07-06\n",
       "Virgin Islands            2009-07-06  2009-07-06\n",
       "West Bank and Gaza Strip  2009-06-15  2009-07-06\n",
       "Yemen                     2009-06-17  2009-07-06\n",
       "\n",
       "[148 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_df = data_df.groupby('Country')['Date'].agg(['min','max']).rename(columns={'min':'first','max':'last'})\n",
    "date_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df[\"Date\"] = pd.to_datetime(data_df[\"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2009-07-06</td>\n",
       "      <td>Austria</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2485</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2489</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                   Country  Total Cases  Total Deaths\n",
       "2355 2009-07-06                   Algeria            5             0\n",
       "2356 2009-07-06       Antigua and Barbuda            2             0\n",
       "2357 2009-07-06                 Argentina         2485            60\n",
       "2358 2009-07-06                 Australia         5298            10\n",
       "2359 2009-07-06                   Austria           19             0\n",
       "...         ...                       ...          ...           ...\n",
       "2485 2009-07-06                 Venezuela          206             0\n",
       "2486 2009-07-06                  Viet Nam          181             0\n",
       "2487 2009-07-06            Virgin Islands            1             0\n",
       "2488 2009-07-06  West Bank and Gaza Strip           60             0\n",
       "2489 2009-07-06                     Yemen            8             0\n",
       "\n",
       "[148 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df = data_df.loc[data_df.groupby('Country').Date.idxmax()]\n",
    "total_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8932259236040596, 1.5174056966476106e-52)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylim(0, 30)\n",
    "plt.xlim(0, 10000)\n",
    "plt.scatter(total_df[\"Total Cases\"], total_df[\"Total Deaths\"], marker=\"o\", facecolors=\"red\", edgecolors=\"black\", alpha=0.75)\n",
    "coefficient = pearsonr(total_df[\"Total Cases\"], total_df[\"Total Deaths\"])\n",
    "plt.title(\"Total Cases vs Total Deaths\")\n",
    "plt.xlabel(\"Total Cases\")\n",
    "plt.ylabel(\"Total Deaths\")\n",
    "print(coefficient)\n",
    "plt.savefig('output/h1n1_fig1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global total deaths: 429\n",
      "Global total cases: 94577\n"
     ]
    }
   ],
   "source": [
    "sum_death = total_df[\"Total Deaths\"].sum()\n",
    "sum_total = total_df[\"Total Cases\"].sum()\n",
    "print(f\"Global total deaths: {sum_death}\")\n",
    "print(f\"Global total cases: {sum_total}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Death rate: 0.004535986550641276\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_total = total_df[\"Total Cases\"].sum ()\n",
    "global_death = total_df[\"Total Deaths\"].sum()\n",
    "\n",
    "global_data = [global_total, global_death]\n",
    "\n",
    "xaxis = np.arange(len(global_data))\n",
    "\n",
    "plt.bar(xaxis, global_data, color='r', alpha=1, align=\"center\", tick_label=(\"Total Cases\", \"Total Deaths\"))\n",
    "plt.title(\"H1N1 GLOBAL CASES AND DEATHS\")\n",
    "plt.ylabel(\"# of cases\")\n",
    "plt.savefig('output/h1n1_fig2')\n",
    "\n",
    "print(f\"Death rate: {global_death/global_total}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2482</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>33902</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>10262</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Canada</td>\n",
       "      <td>7983</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>7447</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Chile</td>\n",
       "      <td>7376</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2358</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5298</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2485</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2076</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>China</td>\n",
       "      <td>2040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                   Country  Total Cases  Total Deaths\n",
       "2482 2009-07-06  United States of America        33902           170\n",
       "2431 2009-07-06                    Mexico        10262           119\n",
       "2373 2009-07-06                    Canada         7983            25\n",
       "2481 2009-07-06            United Kingdom         7447             3\n",
       "2376 2009-07-06                     Chile         7376            14\n",
       "2358 2009-07-06                 Australia         5298            10\n",
       "2357 2009-07-06                 Argentina         2485            60\n",
       "2473 2009-07-06                  Thailand         2076             7\n",
       "2377 2009-07-06                     China         2040             0\n",
       "2416 2009-07-06                     Japan         1790             0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_total_df = total_df.sort_values(by=['Total Cases'], ascending=False)\n",
    "top_10 = sorted_total_df.iloc[0:10,:]\n",
    "top_10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries_10 = top_10[\"Country\"]\n",
    "total_cases_10 = top_10[\"Total Cases\"]\n",
    "\n",
    "plt.bar(countries_10, total_cases_10, color='r', alpha=1, align=\"center\")\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"H1N1 TOP 10 COUNTRIES IN TOTAL CASES\")\n",
    "plt.xlabel(\"COUNTRIES\")\n",
    "plt.ylabel(\"# OF CASES\")\n",
    "plt.savefig('output/h1n1_fig3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2482</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>33902</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>10262</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2485</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Canada</td>\n",
       "      <td>7983</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Chile</td>\n",
       "      <td>7376</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2358</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5298</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2076</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2483</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>195</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>7447</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>2009-07-06</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>277</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date                   Country  Total Cases  Total Deaths\n",
       "2482 2009-07-06  United States of America        33902           170\n",
       "2431 2009-07-06                    Mexico        10262           119\n",
       "2357 2009-07-06                 Argentina         2485            60\n",
       "2373 2009-07-06                    Canada         7983            25\n",
       "2376 2009-07-06                     Chile         7376            14\n",
       "2358 2009-07-06                 Australia         5298            10\n",
       "2473 2009-07-06                  Thailand         2076             7\n",
       "2483 2009-07-06                   Uruguay          195             4\n",
       "2481 2009-07-06            United Kingdom         7447             3\n",
       "2380 2009-07-06                Costa Rica          277             3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_death_df = total_df.sort_values(by=['Total Deaths'], ascending=False)\n",
    "top_10_deaths = sorted_death_df.iloc[0:10,:]\n",
    "top_10_deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries_deaths_10 = top_10_deaths[\"Country\"]\n",
    "total_deaths_10 = top_10_deaths[\"Total Deaths\"]\n",
    "total_cases_10_2 = top_10_deaths[\"Total Cases\"]\n",
    "\n",
    "# indx = np.arange(len(countries_deaths_10))\n",
    "\n",
    "# bar_width = 0.25\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# bar_total = ax.bar(indx-bar_width/2, total_deaths_10, bar_width, label=\"Top 10 deaths\")\n",
    "# bar_death = ax.bar(indx+bar_width/2, total_cases_10_2, bar_width, label=\"Top 10 total\")\n",
    "\n",
    "plt.bar(countries_deaths_10, total_deaths_10, color='r', alpha=1, align=\"center\")\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"H1N1 TOP 10 COUNTRIES IN TOTAL DEATHS\")\n",
    "plt.xlabel(\"COUNTRIES\")\n",
    "plt.ylabel(\"# OF CASES\")\n",
    "plt.savefig('output/h1n1_fig4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3722.12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population1 = data_df[data_df[\"Country\"] == \"Mexico\"]\n",
    "population1[\"Total Cases\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8370.64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population2 = data_df[data_df[\"Country\"] == \"United States of America\"]\n",
    "population2[\"Total Cases\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_onewayResult(statistic=17.152531689789846, pvalue=3.1696378474235495e-12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
<<<<<<< HEAD
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19117e48>"
=======
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a44ee10>"
>>>>>>> fecbb587beaf0f2ee3ea0887a9ec9be658ed98fa
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "US = data_df.loc[data_df[\"Country\"] == \"United States of America\"][\"Total Cases\"]\n",
    "mexico = data_df.loc[data_df[\"Country\"] == \"Mexico\"][\"Total Cases\"]\n",
    "canada = data_df.loc[data_df[\"Country\"] == \"Canada\"][\"Total Cases\"]\n",
    "UK = data_df.loc[data_df[\"Country\"] == \"United Kingdom\"][\"Total Cases\"]\n",
    "chile = data_df.loc[data_df[\"Country\"] == \"Chile\"][\"Total Cases\"]\n",
    "\n",
    "print(stats.f_oneway(US, mexico, canada, UK, chile))\n",
    "\n",
    "countries_boxplot = data_df.loc[(data_df[\"Country\"] == \"United States of America\") | (data_df[\"Country\"] == \"Mexico\") |\n",
    "                                (data_df[\"Country\"] == \"Canada\") | (data_df[\"Country\"] == \"United Kingdom\") |\n",
    "                                (data_df[\"Country\"] == \"Chile\")]\n",
    "\n",
    "countries_boxplot.boxplot(\"Total Cases\", by=\"Country\", figsize=(20, 10))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=6.122546150825157, pvalue=1.5147177084520712e-07)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = data_df[\"Total Cases\"]\n",
    "sample = data_df[data_df[\"Country\"] == \"United States of America\"]\n",
    "stats.ttest_1samp(sample[\"Total Cases\"], population.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Total Cases</th>\n",
       "      <th>Total Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>647</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>183180</td>\n",
       "      <td>1637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>527905</td>\n",
       "      <td>2479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>261451</td>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Total Cases  Total Deaths\n",
       "0      4          647            30\n",
       "1      5       183180          1637\n",
       "2      6       527905          2479\n",
       "3      7       261451          1143"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = data_df.groupby(\"Date\").sum()\n",
    "grouped_data_df = grouped_data.reset_index()\n",
    "grouped_data_df[\"Month\"] = pd.DatetimeIndex(grouped_data_df['Date']).month\n",
    "month_data_df = grouped_data_df.groupby(\"Month\").sum()\n",
    "month_data_df = month_data_df.reset_index()\n",
    "month_data_df"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 21,
=======
   "execution_count": 20,
>>>>>>> fecbb587beaf0f2ee3ea0887a9ec9be658ed98fa
   "metadata": {},
   "outputs": [
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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\n",
=======
      "image/png": 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3xpgg8J85SRzMzWf0oC6BDsUEmCV2Y4yp4TamHmDKgk1cd2obOjVtEOhwTIBZYjfGmBruXzNXEx5ahz9fEB/oUEw1YIndGGNqsKWb05nx63ZGnNORpg0jAh2OqQYssRtjTA2lqvzz85XENajLH87uGOhwTDVhid0YY2qor1bsZPHGdO69MJ76dQO2QripZiyxG2NMDfXiV2s4rkl9rktoU3phU2tYYjfGmBpofcp+Vu3I4ObT2hEaYn/KzSH222CMMTXQVyt2AnBBt2YBjsRUN5bYjTGmBpq1YicntIyidUxkoEMx1YwldmOMqWF2ZWSxdHM6/bvZjV7MkSyxG2NMDTN75S5Uof8J1gxvjmSJ3RhjaphZy3fQJrYeXZo3DHQophqyxG6MMTXI/uw8vl+XRv9uze3WrMYvS+zGGFODfLcmhZy8Ai600fCmGJbYjTGmBpm1fAcxkWEktIsJdCimmrLEbowxNURufgGzV+2iX9dmtiiNKZb9ZhhjTA2xcP1uMrLy6G/N8KYEltiNMaaGmLViBxFhdTg7vkmgQzHVmCV2Y4ypAVSVWct3ck58E+qFhwQ6HFONWWI3xpga4Nete9mxL8tGw5tSWWI3xpga4KsVO6kj0K+rJXZTMkvsxhhTA8xavpNT28cSWz880KGYai6giV1EokXkfRFZJSIrReR0EYkVka9EZK33M8YrKyLyiogkicgvInKKz3mGeuXXishQn+29RORX75hXxJZpMsbUQBtTD7B6Zwb9T7CbvpjSBbrG/jLwpap2AboDK4HRwGxVjQdme68BBgHx3mMEMBZARGKBR4E+QG/g0cKLAa/MCJ/jBlbBZzLGmApVeO91m+ZmyiJgiV1EooBzgAkAqpqjqnuAy4FJXrFJwGDv+eXAZHUWANEi0gIYAHylqrtVNR34Chjo7YtS1R9VVYHJPucyxpgaY9aKHXRtEUWbWLv3uildIGvsHYEU4E0R+UlEXheR+kAzVd0O4P1s6pVvBWzxOT7Z21bS9mQ/248gIiNEJFFEElNSUo79kxljTAX5ISmVxE3pVls3ZRbIxB4KnAKMVdWewAEONbv7469/XI9i+5EbVceraoKqJjRpYgs/GGOqh9U7MrhjyhI6NWnAbWd1CHQ4poYIZGJPBpJVdaH3+n1cot/pNaPj/dzlU76Nz/GtgW2lbG/tZ7sxxlR7O/dlceubi6gXFsLE23rTqF5YoEMyNUTAEruq7gC2iEhnb1M/YAUwHSgc2T4U+MR7Ph24xRsdfxqw12uqnwn0F5EYb9Bcf2Cmty9DRE7zRsPf4nMuY4yptvZn53Hrm4vZczCXN4adSqvoeoEOydQgoQF+/z8Bb4tIOLAeuBV3sfGuiAwHNgPXeGU/By4CkoBMryyqultEngQWe+WeUNXd3vORwESgHvCF9zDGmGorN7+AUW8vZfXODF4fmsCJrRoFOiRTw4gbMG4KJSQkaGJiYqDDMMbUQqrK3z78lamLt/DPK0/i+t5tAx1SmYnIElVNCHQcJvDz2I0xxnhenZvE1MVbuOu8TjUqqZvqxRK7McZUA+8mbuH5WWu4smcr7ut/fKDDMTVYuRK7N0DNlmU1xpgK9MWv2xn9wS+cHR/HM1edjP2ZNcei2MQuIv9PRLp4z+uKyFxgHW462gVVFaAxxgSzeWtTuGfqMnq0iWbczb0ID63ihtT8fPj0U7j4YtiypfTyptor6TfoOmC197xw+lkT4Fzg6coMyhhjaoOlm9O5460ldGxSnzeH9SYyvAonKu3aBf/8Jxx3HFx2Gfz0E6xdW3XvbypNSYk9Rw8NmR8ATFXVfFVdSeCnyRljTI22asc+hr2xiKYN6zJ5eG8aRVbBAjSq8P33cOON0Lo1PPSQS+zvvw+bNsH551d+DKbSlZSgs0XkRGAncB5wv88+uxOBMcYcpU1pB7h5wiLqhYfw1vA+NG0YUblvuH8/vP02jBkDv/wCUVEwciT88Y/QtWvlvrepciUl9j/jlnltArykqhsAROQi4KcqiM0YY4LOrn1Z3DRhIXn5Bbx7x+mVe8e25cth7FiYPBkyMqBHDxg3ztXY69evvPc1AVVsYvdujdrFz/bPcavAGWOMKadnv1xNSkY200acTnyzhhX/Bjk58PHHrnb+7bcQHg7XXQejRkGfPmAj7oNeSaPi/+3z/J4i+yZWYkzGGBOUdmVk8enP27g2oQ3d20RX7Mm3bIFHHoG2bV0i37wZnnsOtm51NfbTTrOkXkuU1BR/js/zocDLPq9PrpxwjDEmeE1ZsJmc/AKGndG+Yk5YUACzZ7va+fTpbnDcxRe72vmAAVDH1iCrjUpK7FLMc2OMMeWUlZvP2ws20a9LUzo2aXBsJ0tPh4kTXf/52rXQpAk8+CCMGAHt21dEuKYGKymx1/Fug1rH53lhgg+p9MiMMSaITF+2jbQDOdx2VoejP0lioqud/+9/kJUFZ54Jjz4KV18NdetWXLCmRispsTcClnAomS/12We3hDPGmDJSVd74fgNdmjfkjOMal+/ggwdh2jSX0BcvdqPZhw5109W6d6+cgE2NVtKo+PZVGIcxxgStH9elsWpHBs+VZx34tWvhv/+FN990Te/dusF//gM33wyN7B7tpnjFJnYRaQfsUdW93uvzgMHARuBVVc2pkgiNMaaGmzB/A43rh3NZj5YlF8zLg88+c33ns2ZBaChceaWrnZ97ro1qN2VS0pDJd4H6ACLSA3gP2Az0AMZUfmjGGFPzbUg9wOxVu7jxtHZEhBUzPGnHDnjqKejYEa64AlasgCeecFPWpk2Dvn0tqZsyK6mPvZ6qbvOe3wS8oaoviEgdYFnlh2aMMTXfxO83EB5Sh5tOa3v4DlWYN8/1nX/wgautX3ABvPIKXHKJq60bcxTKOt3tfOBvAKpaYPcKNsaY0u09mMt7S5K5tHvLQ+vB79sHU6a4hL58OURHw5/+5NZtP/74wAZsgkJJiX2OiLwLbAdigDkAItICsP51Y4wpxbTFm8nMyee2s9rDr7+6ZD5lirspyymnwIQJMGQIRNp9tUzFKe0mMNcBLYCzVDXX294ceKiyAzPGmJosL7+Ad75by19SEznhumdg/nyIiHDLvY4cCb17W7+5qRQlTXdTYKqfXfWBy4FZlRWUMcbUaJs2sfHJF3jvf2/RJHOPu+f588/DsGHQuJzz2I0ppzKNzvBGxd8AXAtsAD6ozKCMMabGKShwU9TGjEFnzKCDwo9dTyP2hf9HSP8Lbd12U2VKmsd+PDAEuB5IA6YBoqrnVVFsxhhT/aWluUVk/vtfWLcOmjXj68HDeazZ6fzrvksJOS4u0BGaWqakGvsqYB5wqaomAYjIvVUSlTHGVGeqsGiRW0hm6lTIzoZzzoF//IMP2p3KfR+v5O5+8ZxhSd0EQEltQ1cBO4C5IvKaiPTD7vJmjKnNMjPdSPaEBHd/8w8/hOHD3Yj3b78l6byLeXjGGvp0iOWefvGBjtbUUiUNnvsI+EhE6uOWkr0XaCYiY4GPVNUGzxljaofVq13tfOJE2LsXTjzRvb7xRmjYEHC3Zb3rnaXUCw/h5SE9Calj9SATGKWO5lDVA6r6tqpeArQGfgJGV1QAIhIiIj+JyGfe6w4islBE1orINBEJ97bX9V4nefvb+5zjb9721SIywGf7QG9bkohUWMzGmFogL8/VyC+4ALp0cXPQL7rIrRb3yy9uQRkvqQM8+dkKVu3I4IVru9O8UUQAAze1XbGJXUROFZFBvttUdTeuef6vFRjDPcBKn9fPAi+pajyQDgz3tg8H0lW1E/CSVw4R6YYb5HcCMBAY410shACvAoOAbsD1XlljjCnetm3w+OPQrh1cdRWsWQP/+Ads2QLvvANnnXXE/PMZv2zn7YWbueOcjpzXuWmAAjfGKanG/i8OT7iFVnj7jpmItAYuBl73Xgtu+dr3vSKTcN0A4ObOT/Kevw/088pfDkxV1WxV3QAkAb29R5KqrvfuRDfVK2uMMYdThblz4ZproG1beOwxOPlk+OQT2LABHnoImjXze+jmtExGf/ALPdtGc/+AzlUbtzF+lDQqvrGqbiy6UVWTRKSiVlj4N/AAUNie1Rh3q9g873Uy0Mp73grY4sWQJyJ7vfKtgAU+5/Q9ZkuR7X38BSEiI4ARAG3btvVXxBgTjPbuhcmTXTP7qlUQGwt//rNrZu/UqdTD92fnMeqdJYjAK0N6EhZic9VN4JV4d7cS9tU/1jcWkUuAXaq6RET6Fm72U1RL2Vfcdn//w9TPNlR1PDAeICEhwW8ZY0wQWbbMDX6bMsWNdO/d2w2Mu/ZaqFfSn75DsnLz+cOkRFZuz+C1W3rRJtbWezfVQ0mJ/WsR+QfwsLe8LAAi8jjeDWGO0ZnAZSJyERABROFq8NEiEurV2lsDhbeOTQbaAMkiEgo0Anb7bC/ke0xx240xtU1WFrz/vqud//ijW7f9hhtg1Cjo1atcp8rNL+Cud5ayYEMaL13bg/O7+G+mNyYQSmo3ug/oCCSJyAfeIwnoDPzlWN9YVf+mqq1VtT1u8NscVb0RmAtc7RUbCnziPZ/uvcbbP8e74JgODPFGzXcA4oFFwGIg3htlH+69x/RjjdsYU8Ns2ACjR0ObNnDzzZCaCi+95AbJTZhQ7qSeX6Dc9+7PfL1yF09efiKDe7Yq/SBjqlBJ89gP4EaSd8SNOAdYrqrrKzmmB4GpIvIUbmrdBG/7BOAt7+JiNy5Ro6rLvdvLrgDygDtVNR9ARO4CZgIhwBuqurySYzfGVAf5+fDll652/sUXbhT75Ze72vn55x/1uu2qysMf/8b0n7fx4MAu3HRauwoO3JhjJz6t7AbXx56YmBjoMIwxRyMlxdXCx42DjRuheXP4wx9gxAho3fqYTq2qPPPFKsZ9t55RfY/jgYFdKibmICEiS1Q1IdBxmDLe3c0YY6otVddnPmYMvPce5ORA377w3HMweDCEhVXI27w6N4lx363nltPb8Veb1maqMUvsxpiaaf9+t2DMmDHw888QFQV33OGmqnWr2LWoFq5P4/lZa7iyZyseu/QERGy5WFN9lXTb1itV9UPveYyqplddWMYYU4yVK91UtUmTYN8+6N7dNb3fcAM0aFDhb1dQoDz9+UpaNIrg6StPoo6tAW+quZJq7A8DH3rPZwOnVH44xhjjR24ufPyxS+hz50J4uJtzPmqUu8taJdagP/1lGz8n7+WFa7oTERZSae9jTEUpKbFLMc+NMaZqJCfDa6+5x/btbv32Z56B226DJk0q/e2z8/L518zVdGsRxRU2rc3UECWuPCciPXFz3SO8578neFVdWtnBGWNqoYICmDPH1c4/+cS9HjTIJfeBAyGk6mrNk3/YRHL6QaYMP9ma4E2NUVJi3w686D3f4fMc3NKs51dWUMaYWig93fWbjx3r7qgWFwf33+8GxHXoUOXh7MnM4T9z1tK3cxPOio+r8vc35miVtEDNeVUZiDGmllqyxCXzd96Bgwfh9NPdjVmuucYt+xog/5mTxP7sPP42qGvAYjDmaJQ43U1EmgJ34laeU9zqbq+q6q4qiM0YE6wOHoR333VT1RYtgshIt9zryJHQo0ego2NzWiaTf9zINb3a0Ll5w1LLG1OdFLuuooiciVtvHWAyMMV7vsjbZ4wx5bNuHfz1r24VuGHD3HS1V15x67aPG1ctkjrAszNXEVqnDn/pf3ygQzGm3Eqqsb8ADFbVn3y2fSIiHwHjKObe5sYYc5j8fJgxw9XOZ86E0FC44go3Ve3ccyt1qtrRWLo5nRm/bOfufvE0iwpcV4AxR6ukxB5VJKkDoKrLRMTapowxJdu589C67Zs3Q8uW8PjjcPvt7nk1pKo8PWMlcQ3qcsc5HQMdjjFHpcR57P5WnBORWEq+3asxprZShfnzXe38gw/cwjL9+sGLL8Jll1XYuu0VKSMrl8Ubd/N9UhrfJ6WyakcGT19xEvXr2orbpmYq6Tf3JWCWiNwPFM5Z7wU86+0zxhgnIwOmTHEJ/bffoFEjuPNOt2575+p1w5S8/AKWbt7Dt2t28cO6NH5J3kt+gRIeWoeEdjE8fHFXrju1TaDDNOaolTTdbbyIbAOe5PBR8U+p6qdVFJ8xpjr77Tc3VW3yZHdTlmK0O7IAACAASURBVJ494fXXYcgQqF8/0NH9bk9mDt+uSWHOql18szqFvQdzCakjdG/diJHnHscZnRpzStsYWzLWBIUS25pU9TPgsyqKxRhTE+TkwIcfutr5vHlQty5cd50bDNe7d7UZDKeqfLxsK/9btIUlm9LJL1Aa1w/nwm7N6NelKWfFx9Ewovp1DRhzrKwTyRhTNps3w/jxrka+cyd07Aj/+hfceis0bhzo6A5zMCefhz/+jQ+WJhPftAGj+h7H+V2a0r11tC0Na4KeJXZjTPEKCuDrr13t/NNP3eC4iy92/ef9+0Od6jeOdl3KfkZNWcqaXRn8+YJ4/nR+PCGWzE0tYondGHOktDSYONH1n69b5+6k9uCDMGIEtG8f6OiK9dkv23jw/V+oGxbCpFt7c87xlX8HOGOqm1ITu4g8rKpPec/rqmp25YdljKlyqrB4saudT5sGWVlw1lnw5JNw5ZWuL72ayskr4OnPVzLxh42c0jaaV288hRaN6gU6LGMCotjELiIPAPOAq4GnvM0/AqdUQVzGmKqSmQlTp7qEvmQJNGjglnsdORJOPjnQ0ZUqbX82f5icyNLNexh+VgdGD+pCWEj16yIwpqqUVGNfDVwDdBSRecBKoLGIdFbV1VUSnTGm8qxZA//9L7z5JuzZAyecAK++CjfdBFFRgY6uTJLTM7llwiK27jnImBtP4aKTWgQ6JGMCrqTEng48BPT1Hl2BAcBoL7mfUenRGWMqVl6eGwQ3ZowbFBcW5prZR42Cs8+uNlPVymL1jgxueWMhB3PymXJ7H05tHxvokIypFkpK7AOBR4HjgBeBn4EDqnprVQRmjKlA27e7aWrjxsHWrdCmDTz1FAwfDs2bBzq6ckvcuJvbJi6mXngI7/7xdLo0rxktDMZUhZJWnnsIQER+xt2ytSfQRETmA+mqemnVhGiMOSqq8N13rnb+4Yeutt6/v2tuv/hid5e1Gmj2yp2MenspraLrMem23rSJjQx0SMZUK2X5nz1TVRcDi0VkpKqeJSJxlR2YMeYo7d0Lb73lpqqtWAExMXD33W7d9vj4QEd3TN5L3MLoD3/lhJZRvDnsVBo3qL4j9Y0JlFITu6o+4PNymLcttbICMsYcpZ9/dsl8yhQ4cABOPdUNjLvuOqhXs6d+5eUX8NzM1Yz/bj1nx8cx9qZeNLC7rxnjV7nmhKjqzxX1xiLSRkTmishKEVkuIvd422NF5CsRWev9jPG2i4i8IiJJIvKLiJzic66hXvm1IjLUZ3svEfnVO+YVkRo0MsiYssjOhrffhjPPhB49YNIkuPZaWLTIPYYNq/FJfde+LG54bSHjv1vPzae1Y8LQUy2pG1OCQP7vyAPuU9WlItIQWCIiX+FaBWar6jMiMhoYDTwIDALivUcfYCzQx7s//KNAAu4OdEtEZLp3H/mxwAhgAfA5bkDgF1X4GY2pHBs3uoFwEyZASoprYn/xRRg6FGKDZ3T4gvVp3PXOTxzIzuPlIT24vEerQIdkTLUXsMSuqtuB7d7zDBFZCbQCLsdNrwOYBHyDS+yXA5NVVYEFIhItIi28sl+p6m4A7+JgoIh8A0Sp6o/e9snAYCyxm5qqoABmznSD4WbMcFPTLrvMTVXr169artt+tFSVcd+t518zV9OucSTv/KEPxzdrGOiwjKkRqkV7loi0x426Xwg085I+qrpdRJp6xVoBW3wOS/a2lbQ92c92f+8/Alezp23btsf2YYypaKmp8MYbbjGZDRugWTP4+9/duu1t2gQ6ugqXtj+b0R/+ylcrdnLxSS149uqTrendmHII+P8WEWkAfAD8WVX3ldAN7m+HHsX2IzeqjgfGAyQkJPgtY0yVUoWFC13t/N13XV/6uefCM8/A4MEQHh7oCCtcZk4eE+ZtYNx368nKzeeRS7px25ntsaExxpRPQBO7iIThkvrbqvqht3mniLTwaustgF3e9mTAt3rSGtjmbe9bZPs33vbWfsobU30dOADvvOMS+rJl0LAh3H67W7f9hBMCHV2lyM0vYNriLbw8ey0pGdn079aMBwZ2plNTa3o35mgELLF7I9QnACtV9UWfXdOBocAz3s9PfLbfJSJTcYPn9nrJfybwdOHoeaA/8DdV3S0iGSJyGq6J/xbgP5X+wYw5GqtWualqEyfCvn1w0kmu6f2GG1xyD0Kqyue/7uD5WavZkHqAU9vH8N+bTqFXu+AZ/GdMIASyxn4mcDPwq4gs87Y9hEvo74rIcGAz7kY04Ea1XwQkAZnArQBeAn8SWOyVe6JwIB0wEpgI1MMNmrOBc6b6yM2FTz5xtfO5c9267ddc4wbDnXFGjVq3vbwWrk/j6S9W8fOWPcQ3bcDrtyTQr2tTa3Y3pgKIG2RuCiUkJGhiYmKgwzDBbOtWeO01GD/ereHerh3ccYdbt71p09KPr8GSdmXwzBer+XrlTppHRfCXC4/nql6tCaljCb2mE5ElqpoQ6DhMNRg8Z0ytoOpq5WPGwMcfu6lrAwe65D5oEISEBDrCSrUrI4t/f72WaYu3UC8shL8O6MxtZ3agXnhwf25jAsESuzGVac8emDzZ9Z+vWuUWj/nLX1wN/bjjAh1dpcvKzee179Yz9tt15OQVcPNp7fjT+Z1sjXdjKpEldmMqw9KlLpm/8w5kZkKfPm6512uuqfFLvJbVd2tSeHT6cjakHmDgCc15cFAXOsTVD3RYxgQ9S+zGVJSsLHjvPdfcvmCBS+A33uimqp1ySunHB4kde7N4csYKZvyynQ5x9XlreG/Ojm8S6LCMqTUssRtzrNavP7Rue1oadO4ML78Mt9wC0dGBjq7K5OUXMPGHjbz01RryCpT7LjyeEed2pG6o9aMbU5UssRtzNPLz4YsvXO38yy/dOu2DB7upauedF9RT1fZn55GcnsmW3QfZsjuTLd7zVTv2kZx+kPM6N+Hxy06kbePIQIdqTK1kid2Y8ti169C67Zs2QcuW8OijbnW4VsFx57GcvAK2pGeyOc0l7eT0g78n8uT0TNIzcw8rHxkeQpuYSLo0b8gjl3Sjf7dmNh/dmACyxG5MaVThhx9c7fy999zCMuefD88/D5df7haWqYGycvNZujmdFdv2sSktk41pB9iYdoCt6Qcp8FneIjy0Dq1j6tE6JpKTWzeiVUw92sRE0iY2kjYx9YitH26J3JhqxBK7McXZvx/eftsl9F9+gagoNxDuj3+Erl0DHV25ZebksXTTHhZuSGPB+jR+3rKXnPwCAKIiQukQV5+ebWK4omdr2jeOpF3j+rSJrUdc/brUsQVkjKkxLLEbU9Ty5W6q2uTJkJEBPXq4hWRuuAHqV7/pWqpKcvpBlm5OZ9mWPezNzOVgbr575OSTlZtPZk4+G1IPkFeghNQRTmwZxbAz23Nax1h6tIkhJjLMat3GBAlL7MYA5OTARx+5hP7tt+62qNdd5wbD9elTbQbDqSp7D+ayIfUASzfvYcmm3SzZlM7OfdmA6+9u3CCcemEh1AsLISIshOjIcFo0CqFf12b06RhLQrsYGkbUzO4DY0zpLLGb2m3LFlcbf+012LkTOnSAZ5+FW2+FJoGbe52TV0Dipt38tHkPW/ccZGv6QbbtOcjWPQfJzMn/vVyr6Hqc1rExvdrF0KtdDF2aR9m668bUcpbYTe1TUACzZ7u+8+nT3eC4iy92/ecDB7qpawGwa18W36xOYc6qXcxPSmV/dh4AMZFhtIqpR8cm9TkrPo5W0fVoExtJ99bRNG8UEZBYjTHVlyV2U3ukp7v7nY8dC2vXQlwcPPCAW7e9fftKfWtVpUBhT2YOO/dlszMji137stzzfVks27KH5dv2AdA8KoJLu7egb+emnH5cY6Ks2dwYUw6W2E3wS0x0tfP//c8t+3r66W7u+dVXQ91jvxmJqrIh9QDzk1KZtzaVxI27yczJp8BL5gWqlHR35Nj64XRq0oAHBnbmvM5N6dK8oQ1kM8YcNUvsJjgdPAjTprmEvnixG80+dKhrbu/e/ZhPvzczl+/WpjB/bSrzk1LZuucgAG1i69G/W3Oi64dRR4Q6gvdTEIHoemE0i4qgaVQEzaLq0qRhXVty1RhToSyxm+Cydq1bFe7NN13Te9eu8J//wM03Q6NGR31aVSVp135mr9rFnFW7WLIpnfwCJSoilDOOi2Nk3+M4Oz6Odo2r33Q4Y0ztYond1Hx5efDZZ67vfNYsCA2FK65wU9XOPfeop6qlZGSzdHM6P65LY/aqnWzZ7Wrl3VpEMarvcZzXpSndW0fbKHRjTLViid3UXDt2wOuvuzurJSe7tdqfeMKt296iRblOlZtfwOodGSzZlM7Sze5RmMgjwupwVqc4Rp7bifO6NKFFo9pxP3VjTM0UmHk9xhwtVfjuOxgyBNq0gUcecc3tH34IGze618Uk9ey8fFbvyODL33Yw/rt1/P2jX7l5wkLOfm4OXR75kkv+M59Hpy9nwfo0TmzZiL9f1JUPRp7Osv/Xn9eHnsoNfdpaUq8tkpLcbInu3SEkBPr2Lf2YP//ZtQ7df//h2997Dy67zF14NmgAvXq5gZxF7dvnztG+PURGut/rf/+bEkdeFnrtNTj+eDcYtGtXmDLl8P2rV8Odd7p9kZHQsSPccw/s2VP6uQNNZBQiMxBJQ0QR6VtMuVBERiOyFpFsRJIRealImWhE3kBkNyL7EfkCkU5FynzjvY+/x+lemXBE3kVkPSIHEUnxztWrDJ+nuHNn+5Q5AZEvEdnmfZbNiLyOSJlqLFZjNzXDvn3uj9WYMW7J1+ho+NOf3Lrtxx//e7HsvHzS9uewKyObdbv2k5Syn7U797MuZT+b0g4cdnOT6Mgw2sVG0qNNDIN7RBLfrCG92sXQslGEjUqv7ZYvh88/h9NOc6sSlmbFCnfXv6ioI/e9+KJb+Oill9wUy88/d8sTp6a63+FCw4a5i9ann4ZOnWDuXPjLX1xiv/fe4t/7f/9zFyEPPOBuTvTFF3DLLW7A6BVXuDJffQXff+8Gj558MqxfDw8/DD/+CAsWBGzthjK6BVBgJnB9CeXeBPoBjwOrgDZAtyJlpgEnAvcAe4GHgdmInITqPq/MKKDoP+QTQE9gsfc6xIvpn8A6r/y9wBxEeqK6voQ4T/ez7VPge5/XjYANwGRgG9ABeBTohcipqOaVcH5Ey3I1WIskJCRoYmJioMMwhX791fWdv/WWuylLr17oyJEknX8J3yYfYNmWPaRkZJO6P5uUjGz2ZR3++x4WIrRvXJ9OTRv8/ugQV592sfVpFGnzw00xCgoOJburr3ZJ+Jtvii9/wQVuGuVbb7nyzz9/aF9qqkvovm64wSXVDRvc68xMaNjQ1dB9k/2VV8LWrbBwYfHv3bmzW/Z48uTDj1uzBn77zb1OS4PY2MPHm8yaBQMGuM917rnFn7+MRGSJqiZ4LyYCoDqsAk5cB9UCRE4EfgXOQ/WbImUG4pJjd1RXFHOe04EfgH6ozvG2NcMl0P+H6vPFHBcO7ACmoTqyhDgbAGnA31B9sRyf71RgETAE1WkllLsQmAX0QnVpSae0GrupfrKzXdP6mDEwfz5ERJB15dUsGDSET8NbMz8phZ3j3MVX65h6tGxUj87NG3JmpzjiGrgpZE0a1KV9XH3aNY4kLKRa10aMPzNmwKWXwrp1rrZbaMMGOO44+Phj17xdWcpTg33/fVi5Ej75xCX2ooomdYCePV35Qnl57mKi6MyN6Gg3fqQ4mZluJsjf/3749v793b0PNm50TfuNG/uPAWDXLvdzzx448URX6/e9SLjsMli1CpYtc834VU21oAylbgPmFJvUnR5AHvCtz7l3IvILcDHgP7HDQCAG8NN/cpgDQBYQXoZ4fV3vHftpKeXSvJ+lnt8Su6k+Nm1y67a//jrs2sXBtu35YfhfGd/xHBbuE/gNoiN3clanOM6Oj+Os+Ca0irY+76A0cCC0bAmTJsFjjx3aPnGiW8P/oouKPzY/v/R+6Tp1Kqb5+eBBuO8+eOaZ8t3574cfoJtPK3FUFFx7LTz3nGsqP+44dzOid9+FV18t/jzZ2e6zhhf5W1+48NKqVcWvqvjDD+5nYRzR0TBhgvvur7wSBg9200ZnzIB58wKT1MuuDzAdkf/DNd2HAl8Cd6G6zSsTAeShml/k2GygpPswDwG2AvOO2OP67EKAOOA+IJ/SLwCKHn8N8AmqmX721/E+SwfgGVxXwKLSTmuJ3VS53PwCMrPzycrLJys7lzpffUXUm68RNWcWCvx08lm82u9u5rY5mdDQEHrFxfDX05twdnwcJ7RsZNPLaoOQENfnPGmSWyVQxCWwSZPcmgShJfzp6tfPJcWSDB3qLhKO1T//6QZr3nRT2Y+ZPdvV1t944/DtkyfDjTceqkmLuPMPHVr8uWJiXBP74sVuQGmhRd7f/t27/R+XmQmjR7sm+BNOOLR9wAAYMcL12bdr5/r2778fzjij5M+Un++SiUjhP4xw+GuAgjLWvo9Gc2AY8DMuETcEngM+QuQ0XJ9zEhDh9af/6sVXD9fn3tDvWUUigUuB8fjvt34Q188OkAJchOqmcsR9NtAamFrM/s+BAd7zJd75S/0OLbGbCqeq7DuYR/KeTDanZbJpdyab0jLZlHaATWmZbN97kKjMfVzzy9fctOxz2uzZQUpkNP/X5xr+12MA9Tt15Oz4JrwRH0fvDrHUr2u/prXSbbe5gWTffAPnnecGk23a5O68V5Jx4yAjo+Qy/prHy2vDBteXPmdO2ddK2LjR9a9ffrm7cPF1772uL/3NN92o9fnzXWtFXBwMH178Of/4R3j5ZTjzTPc9ffnloS6BED+rGqq68+3a5WrjRb3wghtsd/rpbhDfE0+U/rn69SMXegG5Rfbc4vN8Ei75HulQzff3KP3UrEsi3uNyVNO8c27HNbufD8zGDb7bAIxD5FZgH64W3MhP3IUuBRpQfC18IvA10AI36O4zRM4ppUvA1/VAuhebP38CYoF43EC/LxA5E9Wskk5qfzFNueXkFbBzXxZb97hbibrbiWb9/nzbnoMcyDn8/2Rs/XDaxtTjqrxk+i/5iC7ffk5oTg4pp/ThpyGPsmfAxfSKrMfVTerblDLjdOzoppm9+aZLWG++Cb17H17D9KdTp7I1xR+r0aNh0CDo0uXQtLGCAtc8vmeP6y/3Tfi7d7vybdseOR3tl18OLbB04YVu2znnuAuU++93FzPFxfz3v7t+9quucq9jY90FwV//Cs2aHVn+wQdd//tXX7nvuKgGDeCSS9yKjcOHl+1+CuPGcU6XLiu/O5TIH/V+Pu5TKrWEM5wLzPV5/S3Qt/Q3/l06sP73pO7MB3JwI+Nno5qDyBBckl7lU2YyLvn7MwRIQtX/iGrVHbiBdSDyBbAcGM3hFzT+udaMq4APUPU/9UJ1rfdsISLzcBcmNwBv+C3vCfrELm605Mu4q8HXVfWZAIdUbeTmF7D3YC57MnPZezCXvQdz2JOZS0ZWHvuz88jIyuNA9qHnuw9ks21PFjszso74u9m4fjgtow+/tWjL6Hq0jY2kbT2I+uh9Nxhu6VL3h+P222HkSJqceCKBu+u5qfZuvx3+8AfXJP3hh642WZqqaopfvRp+/tnF5ev//s89tmyB1q3dtsxMlyxzclwtuWh//Covz/Tocfj2nj3dRUJamhtb4E9kpOuL37kTUlLchc1nn7l+91NOObzsSy+5VoapU+Hss/2fLzHRXWT07AlPPQXXXw/Nm5f8XXTuzDzI/D0BirgEW1xCPNIS4FSf16U0uRxhJeDvCkSAQ03Xqou8eevH4/rb1yHyGbDgyCOlETAI16RfOtU8RH4F/Fwt+dUPaEJZ++RVNyGyuyznD+rELiIhwKvAhUAysFhEpmvZm0mqhYICJV+V/AIlN7+A7DzvkZvv/3lePtm57nlmTh67D+Sw+0AOqftz2H0gm7QDOaTtz/n9ft/FCQsRGkaE0aBuKA3qhhJTP4yz4+NoGV3v98TdMjqCltH1iAjz0+S3ejU8/5z7A1o44nbsWNeP2NB/l5Yxh7nySrewypAhrjbs249cnKpqin/9dTcF09eQIa7feuTIQ4k4Lw+uucbVqr//Hpo2PfJc7dq5n0uXun7uQkuWuIuAssTbrJl7FBS4+yVcffXh8+rfeccN9HvhBTdQz5+sLDcHfsAAd7HQvbvrc58+vfT3PxaqGcCxzDP+DHgckThUC1sGzgHCcP3uvu+lwGoAROKBC3BN7kVdgbtYKFviFYkATuHw+egluR5X2/+mjOfvDDTG1dpLFNSJHegNJKm3WICITAUuByo8sb86N4lPf96GKiiuOuueH7oXd4GXnFUh30vWhfvyC9Td5rPAe+09zysopUmxDELrCLH1w4mtH05cg7q0jokktn44MZHhREeGER0ZRqN6hz8aRIQe3V3H8vLcH4ExY9wgobAw9wdm1CjXB2gLv5jyiIhwF4KvvupqjtHRpR/TufOxv29mpltIBtw88n373LQ2cCPyIyMhIcF/vG3aHL5S3ahR7lwvv+ya4xf4VA579nRN3QkJ7nHbba5Pu0MH18f+73+7FeIK/998+61rkZg9+9Dc888+c2MPunZ1/eavveZaACZNOvQ+337r+vT793d9574xtG59qGXh4YfdUs2zZ7vPOGmSq9lPnHjkmICqIpIAtMctOANwLiJxwEafFoHxwN3Ap4g8jRsM9yzwNarzfc71CK4ZPhU4CXgEmIrqV37eeQjwM6or/cR0Pa42/yVuAZnCPvYWwIs+5W7BNZsfd9igOpG6wGBgot/BcCLP46bmLQT24EbtP4BbDKe4gXa/C/bE3grY4vM6GTct4jAiMgIYAdC2bdujeqOYyHDaxkYiAoL8/v+w8HWdOkJI4S086/jczrOOEFJ4e886cuhWn3WE0MJ9Pj/DQoSIsBDqhtahbqj3M8w9jwjz2RYaQt2wOkSEhRAVEVr5K6lt2+b+oIwf7563aeOa8W6/3X8/nzFlNXiwS+y33VZ177lrl6tl+yp8vWFD8VPI/Jk1y/28554j9xWeKyQEPv3UJdYnnnBN6u3aub7y++47VF71yOl8oaGulSIpyV1YDBjgxiO0anWozNy5kJsLM2e6h69HH3Xv8/33rqn+rbcOLct8xhlu9bs//9ktwlN4AVC17gJ8pwY85v08NBhPdR8i5wOv4BJfDvAJbjU4X42Bf+Omp23BzV0/sn/HXTj0wyV+f1YDN+GSeAywHZeEE1Bd7lOuDq4buOgf4EG4QXvFJelE3MC5EbhpepuBD4B/onqgmGMOhR/MK8+JyDXAAFW93Xt9M9BbVf9U3DG28lw5qLoRy2PGuME4+fluDuzIkXDxxf5H5BpTXg88ANOmuSRYvZc+rdUOW3nOBFSw19iTOdR8A26+4LZiypqy2rvXzbkdM8Y1+cXGuqk6d9zhBu4YUxFWr3ZrsI8d62qVltSNKZNgT+yLgXgR6YBbOWgIbqqAORrLlrk/slOmuD7I3r1d39u110I9m6JmKtgdd7h53ZddBnffHehojKkxgjqxq2qeiNyFm/wfAryhh/d/mNJkZblBQ2PGuJtWRES4BTZGjXK3nzSmspR00xVjTLGCOrEDqOrnuGX5THls2OAG5EyY4O5OFR/vbj85bJhbxtIYY0y1FPSJ3ZRDfr5bjnLMGHdPZxG39OWoUe6OT9bHaYwx1Z4lduOm1rzxhlvUYuNGt8rUI4+4Fb8CM73FGGPMUbLEXlupuj7zMWPgvffcUpd9+7rbRg4e7BaWMcYYU+NYYq9t9u93S0uOGePWuY6KcqOP//jHw+8PbYwxpkayxF5brFzppqpNmuSWx+ze3Q2Ou+EGd1MWY4wxQcESezDLzYVPPnG187lz3d2err3WDYY77TRbt90YY4KQJfZglJzs1m1/7TXYvt2tRf3MM26t7eJu/WiMMSYoWGIPFgUFMGeOq51Pn+5eDxrkbsoyaJCt226MMbWEJfaaLj3d9ZuPHQtr1rj7Nt9/vxsQ16FDoKMzxhhTxSyx11RLlrhk/s47cPCg6zN/6y137/OIiEBHZ4wxJkAssdckWVnu9pVjxsCiRRAZCTff7G6T2qNHoKMzxhhTDVhirwnWrXOrwr3xBuzeDV26wCuvwC23QKNGgY7OGGNMNWKJvbrKz4cZM1ztfOZMCA2FK65wtfO+fW2qmjHGGL8ssVc3O3e6O6qNGwebN0PLlvD443D77e65McYYUwJL7NWBKsyf72rnH3zgFpY5/3x46SW49FJbt90YY0yZWWIPpIwMmDLFJfTffnP95Xfe6dZt79w50NEZY4ypgSyxB8Jvv7mpapMnu5uy9OwJr78OQ4ZA/fqBjs4YY0wNZom9quTkwIcfutr5vHlQty5cd51bt713bxsMZ4wxpkJYYq9smze7ZV1few127YKOHeFf/4Jbb4XGjQMdnTHGmCBjib0yFBTA11+72vmnn7rBcZdc4mrn/ftDnTqBjtAYY0yQssRekXbvhjffdIvJJCW5O6k9+CCMGOHusGaMMcZUMkvsFeXJJ+Hpp92yr2edBU88AVde6frSjTHGmCpiib2itG3r+s1HjoSTTgp0NMYYY2opS+wVZehQ9zDGGGMCyEZxGWOMMUHEErsxxhgTRCyxG2OMMUEkIIldRP4lIqtE5BcR+UhEon32/U1EkkRktYgM8Nk+0NuWJCKjfbZ3EJGFIrJWRKaJSLi3va73Osnb374qP6MxxhgTCIGqsX8FnKiqJwNrgL8BiEg3YAhwAjAQGCMiISISArwKDAK6Add7ZQGeBV5S1XggHRjubR8OpKtqJ+Alr5wxxhgT1AKS2FV1lqrmeS8XAK2955cDU1U1W1U3AElAb++RpKrrVTUHmApcLiICnA+87x0/CRjsc65J3vP3gX5eeWOMMSZoVYc+9tuAL7znrYAtPvuSvW3FbW8M7PG5SCjcfti5vP17vfJHEJERIpIoIokpKSnH/IGMMcaYUAO6MgAACnhJREFUQKm0eewi8jXQ3M+uv6vqJ16ZvwN5wNuFh/kpr/i/ANESypd0riM3qo4HxgMkJCT4LWOMMcbUBJWW2FX1gpL2i8hQ4BKgn6oWJtNkoI1PsdbANu+5v+2pQLSIhHq1ct/yhedKFpFQoBGwu7S4lyxZkioim0opFue9d21jn7t2sc9d+xzLZ29XkYGYoxeQledEZCDwIHCuqmb67JoOvCMiLwItgXhgEa72HS8iHYCtuAF2N6iqishc4Gpcv/tQ4BOfcw0FfvT2z/G5gCiWqjYpQ/yJqppQpg8bROxz1y72uWuf2vzZg0mglpT9P6Au8JU3nm2Bqv5RVZeLyLvAClwT/Z2qmg8gIncBM4EQ4A1VXe6d60Fgqog8BfwETPC2TwDeEpEkXE19SNV8NGOMMSZwApLYvSloxe37/+2dfbBVVRnGf49piumomKD40VXG1PzoamSW2oDjKCqmTTpCUjmNY6bNkFP4UU2ZSQ5ZfjSEkyliKaaVOKU05ggMOqL4dQUEshR0UgM0G6UBR+Ppj7UOLg7nHs5Vb/fefd7fzJ5z1rvXXmu9m3159/o465kETGpgnwXMamB/jrRqvt6+Djj9vbU0CIIgCAYW/WFV/EDk+r5uQB8RfrcX4Xf70c6+Vwa1MO0cBEEQBMEAIXrsQRAEQVAhIrAHQRAEQYWIwN4DuhOiqSKSpklaJWlxYRss6b4suHOfpJ36so29gaQ9Jc2RtFTS05ImZHulfZe0jaQFkp7Kfv8w2xuKLFWNrEnxpKS7c7ryfktaIWmRpC5Jj2VbpZ/zdiECe4tsRoimikwnCfGUXAzcnwV37s/pqvE28C3bBwBHAOfnf+eq+/4mcIztjwOdwGhJR9C9yFLVmAAsLdLt4vco253Fb9er/py3BRHYW6ehEE0ft6nXsD2PTXfqK4V1SsGdymD7ZdtP5O9vkP6z352K++7EmpzcKh+me5GlyiBpD+Ak4IacbiYuVXUq/Zy3CxHYW6c7IZp2YqjtlyEFQGBIH7enV5HUARwKPEIb+J6Ho7uAVSRp5WfpXmSpSlwDXAisz+lm4lJVwsBfJD0u6Zxsq/xz3g701c5zA5GWRWWCgY+k7YA/AN+0/Xo7KP7mXR47Je0IzAQOaJTt/9uq3kXSGGCV7ccljayZG2StlN+ZI22/JGkIaRfQZX3doOD9IXrsrdNMoKZdWClpN4D8uaqP29MrSNqKFNRvtX1nNreF7wC2/w3MJa0x2DGLKEE1n/kjgc9JWkGaXjuG1IOvut/Yfil/riK9yB1OGz3nVSYCe+s8ShaiyStkx5KEZtqJmrAObCy4Uxny/OqNwFLbVxWnKu27pF1yTx1Jg4BjSesLaiJLUEG/bV9iew/bHaS/6dm2z6Tifkv6kKTta9+B44DFVPw5bxdi57keIOlE0tt8TYhmkz3tq4Kk24CRJBnHlcAPgLuAO4C9gBeA021vVgp3ICHpKOABYBHvzLl+hzTPXlnfJR1CWiz1AdIL/x22L5O0D6knO5gksjTe9pt919LeIw/Ff9v2mKr7nf2bmZNbAjNsT5K0MxV+ztuFCOxBEARBUCFiKD4IgiAIKkQE9iAIgiCoEBHYgyAIgqBCRGAPgiAIggoRgT0IgiAIKkQE9qBfIekKSSMlndpMQU/SOZKW5WNB/pla7dzcrMLXlY/TGlxfU7ZaJGmJpMslbV2X5wJJ6yTtkNNDJC2XtGuRZ6qkiyVtK+nWXN5iSQ/m3evq6/1qzrMw5zsl26fX2pnb/1hxzQhJc4v04ZLmZR+XSboh13+WpNWF312NhIok/bcuT0e+5zVls7KcZZIuKK7dL7evS0kB73pJxxdlrSnu/a8b1P1RSbOUFBKXSrpD0tDi/LWSXpS0RWEbKuluJeW5JZJmZXuHpLV1vny5vs4gaDtsxxFHvzmA2cAg4GrSlpeN8owBHgc+nNOHkX5zu2tOzwVGbKaeFcX12wEzgJvr8iwg/ab9rMJ2LnBLUe9CkmDKJcBVRb79gK3rytuDtP/6DkW9e+fv04HTiva/AJyQ0yOAufn7UOB54NM5LdJGKkOBs4ApLdzjNQ1sI4G78/cN5ZD2TX8F2DOn7wVOKa47uK6cbu89sA3wN+DkwjYKOCh/3yL7/TAwssjzS2BCkT4kf3YAi/v6mY0jjv52RI896BdIulLSQuCTwHzgbOA6Sd9vkP0iYKLtVwCc1NhuBs5/N3U7qZqdC5wqaXBuz3BS4P0eMK7Ifj0wXNIoYArwDdtvAbsBLxZl/tWbbmgyBHgDWFOr1/bybpp1Za67nvNJLyDzcxm2/XvbK3vic6vYfhX4O8k/8uc/ivOLelDcF4H5tv9UXD/H9uKcHEXa/ew6Nr7n9XUu7IkPQdBuRGAP+gW2J5KC+XRScF9o+xDblzXIfiCpx17yWLbXuLUYnt25hfpfB5YD+2bTOOA2Uo99PyWhDGyvB75O2kv+GSd5W4BpwEWS5udh/X3ZlKdIu/gtl3STpJObNGk+8GZ+gSg5iE19Lzmjbmh6UIM8g4rzMxuc34CkvUg97VowvRqYLenPeapix2bX97DttXs+ExijtGc/wC+AGyXNkfRdScOKa4bX+Xt0D9oTBJUkAnvQnzgU6AL2B5b08FqxsQLXmbY78/FqD8qoMRb4bQ7kdwKn107Y7iL1LKfW2fYh9bQHA49K2kgdzUk9bTRp6PwZ4GpJlzZpz+U07rU34/bC707baxvkWVuc/3w35Zwh6WngOeBa2+uyDzeRVN9+Rxq+f7h+bcK7QUl/4UTgrvyS9Qhp/3Js30u6t78iPRtPStolX/psnb8PvNe2BMFAJwJ70OdI6lTSAZ8ETATuAUY36XEuAT5RZzuMnr8MlG3YnjRn+4zSvun7kqQsV5CC/Li6S9bzzl7ywIah9TttnwfcQgpU1OWx7QW2r8jlfqG7NtmeTeotH1GYn2ZT33uD220fCBwN/KxcMGj7JdvTbJ8CvE3qibdCs7aPBnYAFuV7fhTFPbf9L9szbH+JJMj02Z46FATtQgT2oM+x3WW7k9SL/RhpAd3xTXqcPwEm14bYJXWSFnxNbZB3s+TV61NJvcXXSAHlUtsd+RgG7C7pI03KOFLSTvn7B7Mfz9flGSbpsMLUWZ+nAZOAC4v0FOArkj5VlDu+DLzvJ3ku/zfAhFzX6NoQea5zZ4q1BZthBvAZSSfVDLm8g0n3/OzaPQf2Bo7Lq/2PkbRtzr89MJy0yC4IggZsufksQdD75KHV12yvl7S/7W5737b/KGl34CFJJi1IG2/75R5WO0eSSC+4M4EfZftY4IS6vDOzfXI3ZQ0nLfarlXcPaR6+ZCvgp3mOeB2wmrRor1tsz5K0ukivlDQ2lzOENGowjzRdAGkI/aiiiPNsP9SsjhaYDDwh6cek4fFrJa3L5yba/mcrhdheK2kMcI2ka4C3SHP3FwHHA18r8v5H0oPAySSlsSmS3ibd2xtsPyqpgzzHXlQzzfbP34OvQTDgCXW3IAiCIKgQMRQfBEEQBBUiAnsQBEEQVIgI7EEQBEFQISKwB0EQBEGFiMAeBEEQBBUiAnsQBEEQVIgI7EEQBEFQIf4HzCAiUAr9Jr4AAAAASUVORK5CYII=\n",
>>>>>>> fecbb587beaf0f2ee3ea0887a9ec9be658ed98fa
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = grouped_data_df.reset_index()\n",
    "test[\"index\"] = test[\"index\"] + 1\n",
    "\n",
    "plt.plot(test[\"index\"], test[\"Total Cases\"])\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(test[\"index\"], test[\"Total Cases\"])\n",
    "regress_values = test[\"index\"] * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.plot(test[\"index\"], regress_values,\"r-\")\n",
<<<<<<< HEAD
    "plt.annotate(line_eq,(10,60000),fontsize=15,color=\"red\")\n",
=======
    "plt.annotate(line_eq,(35,10),fontsize=15,color=\"red\")\n",
>>>>>>> fecbb587beaf0f2ee3ea0887a9ec9be658ed98fa
    "plt.title(\"H1N1 RATE OF SPREAD IN TOTAL CASES\")\n",
    "plt.xlabel(\"# OF DAYS SINCE FIRST CASE\")\n",
    "plt.ylabel(\"# OF CASES\")\n",
    "plt.savefig('output/h1n1_fig5')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test[\"index\"], test[\"Total Deaths\"])\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(test[\"index\"], test[\"Total Deaths\"])\n",
    "regress_values = test[\"index\"] * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.plot(test[\"index\"], regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n",
    "plt.title(\"H1N1 RATE OF SPREAD IN DEATH\")\n",
    "plt.xlabel(\"# OF DAYS SINCE FIRST CASE\")\n",
    "plt.ylabel(\"# OF CASES\")\n",
    "plt.savefig('output/h1n1_fig6')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>14045</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>39519</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Cumulative no. of cases  Cumulative no. of deaths\n",
       "Country                                                               \n",
       "Algeria                                   19                         0\n",
       "Antigua and Barbuda                       12                         0\n",
       "Argentina                              14045                       175\n",
       "Australia                              39519                        39\n",
       "Austria                                  173                         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df = data.groupby(\"Country\").sum()\n",
    "grouped_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14045</td>\n",
       "      <td>175</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>39519</td>\n",
       "      <td>39</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1327</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Cumulative no. of cases  \\\n",
       "0                     Algeria                       19   \n",
       "1         Antigua and Barbuda                       12   \n",
       "2                   Argentina                    14045   \n",
       "3                   Australia                    39519   \n",
       "4                     Austria                      173   \n",
       "..                        ...                      ...   \n",
       "143                 Venezuela                     1327   \n",
       "144                  Viet Nam                      822   \n",
       "145            Virgin Islands                        1   \n",
       "146  West Bank and Gaza Strip                      146   \n",
       "147                     Yemen                       50   \n",
       "\n",
       "     Cumulative no. of deaths Lat Lng  \n",
       "0                           0          \n",
       "1                           0          \n",
       "2                         175          \n",
       "3                          39          \n",
       "4                           0          \n",
       "..                        ...  ..  ..  \n",
       "143                         0          \n",
       "144                         0          \n",
       "145                         0          \n",
       "146                         0          \n",
       "147                         0          \n",
       "\n",
       "[148 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['Lat'] = ''\n",
    "grouped_df['Lng'] = ''\n",
    "grouped_df.reset_index(inplace=True)\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bermuda, UKOT not found. skipping....\n",
      "British Virgin Islands,  UKOT not found. skipping....\n",
      "British Virgin Islands, UKOT not found. skipping....\n",
      "Cayman Islands, UKOT not found. skipping....\n",
      "France, French Polynesia, FOC not found. skipping....\n",
      "France, Martinique, FOC not found. skipping....\n",
      "France, New Caledonia, FOC not found. skipping....\n",
      "French Polynesia, FOC not found. skipping....\n",
      "Guadaloupe, FOC not found. skipping....\n",
      "Martinique, FOC not found. skipping....\n",
      "Netherlands, Curacao, OT not found. skipping....\n",
      "New Caledonia, FOC not found. skipping....\n",
      "United Kingdom, Isle of Man, Crown Dependency not found. skipping....\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.65963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0608</td>\n",
       "      <td>-61.7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14045</td>\n",
       "      <td>175</td>\n",
       "      <td>-38.4161</td>\n",
       "      <td>-63.6167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>39519</td>\n",
       "      <td>39</td>\n",
       "      <td>-25.2744</td>\n",
       "      <td>133.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5162</td>\n",
       "      <td>14.5501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0343</td>\n",
       "      <td>-77.3963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0667</td>\n",
       "      <td>50.5577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>23.685</td>\n",
       "      <td>90.3563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>13.1939</td>\n",
       "      <td>-59.5432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "      <td>50.5039</td>\n",
       "      <td>4.46994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bermuda, UKOT</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>-16.2902</td>\n",
       "      <td>-63.5887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bosnia and Hezegovina</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>43.9159</td>\n",
       "      <td>17.6791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>4085</td>\n",
       "      <td>3</td>\n",
       "      <td>-14.235</td>\n",
       "      <td>-51.9253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>British Virgin Islands,  UKOT</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>British Virgin Islands, UKOT</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>4.53528</td>\n",
       "      <td>114.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>42.7339</td>\n",
       "      <td>25.4858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>12.5657</td>\n",
       "      <td>104.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Canada</td>\n",
       "      <td>88970</td>\n",
       "      <td>208</td>\n",
       "      <td>56.1304</td>\n",
       "      <td>-106.347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country  Cumulative no. of cases  \\\n",
       "0                         Algeria                       19   \n",
       "1             Antigua and Barbuda                       12   \n",
       "2                       Argentina                    14045   \n",
       "3                       Australia                    39519   \n",
       "4                         Austria                      173   \n",
       "5                         Bahamas                       46   \n",
       "6                         Bahrain                      139   \n",
       "7                      Bangladesh                       35   \n",
       "8                        Barbados                       84   \n",
       "9                         Belgium                      505   \n",
       "10                  Bermuda, UKOT                        9   \n",
       "11                        Bolivia                     1200   \n",
       "12          Bosnia and Hezegovina                        2   \n",
       "13                         Brazil                     4085   \n",
       "14  British Virgin Islands,  UKOT                       10   \n",
       "15   British Virgin Islands, UKOT                        1   \n",
       "16              Brunei Darussalam                      283   \n",
       "17                       Bulgaria                       67   \n",
       "18                       Cambodia                       32   \n",
       "19                         Canada                    88970   \n",
       "\n",
       "    Cumulative no. of deaths      Lat      Lng  \n",
       "0                          0  28.0339  1.65963  \n",
       "1                          0  17.0608 -61.7964  \n",
       "2                        175 -38.4161 -63.6167  \n",
       "3                         39 -25.2744  133.775  \n",
       "4                          0  47.5162  14.5501  \n",
       "5                          0  25.0343 -77.3963  \n",
       "6                          0  26.0667  50.5577  \n",
       "7                          0   23.685  90.3563  \n",
       "8                          0  13.1939 -59.5432  \n",
       "9                          0  50.5039  4.46994  \n",
       "10                         0                    \n",
       "11                         0 -16.2902 -63.5887  \n",
       "12                         0  43.9159  17.6791  \n",
       "13                         3  -14.235 -51.9253  \n",
       "14                         0                    \n",
       "15                         0                    \n",
       "16                         0  4.53528  114.728  \n",
       "17                         0  42.7339  25.4858  \n",
       "18                         0  12.5657  104.991  \n",
       "19                       208  56.1304 -106.347  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a params dict that will be updated with new city each iteration\n",
    "params = {\"key\": gkey}\n",
    "\n",
    "# Loop through the cities_pd and run a lat/long search for each city\n",
    "for i,row in grouped_df.iterrows():\n",
    "    base_url = \"https://maps.googleapis.com/maps/api/geocode/json\"\n",
    "    \n",
    "    country = row['Country']\n",
    "    params['address'] = f\"{country}\"\n",
    "    try:\n",
    "        response = requests.get(base_url, params=params).json()    \n",
    "        grouped_df.loc[i, 'Lat'] = response[\"results\"][0][\"geometry\"][\"location\"][\"lat\"]\n",
    "        grouped_df.loc[i, 'Lng'] = response[\"results\"][0][\"geometry\"][\"location\"][\"lng\"]\n",
    "\n",
    "    except:\n",
    "        print(f\"{country} not found. skipping....\")\n",
    "        pass\n",
    "\n",
    "\n",
    "grouped_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Cumulative no. of cases</th>\n",
       "      <th>Cumulative no. of deaths</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>28.033886</td>\n",
       "      <td>1.65963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>17.060816</td>\n",
       "      <td>-61.7964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14045</td>\n",
       "      <td>175</td>\n",
       "      <td>-38.416097</td>\n",
       "      <td>-63.6167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>39519</td>\n",
       "      <td>39</td>\n",
       "      <td>-25.274398</td>\n",
       "      <td>133.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>47.516231</td>\n",
       "      <td>14.5501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>1327</td>\n",
       "      <td>0</td>\n",
       "      <td>6.423750</td>\n",
       "      <td>-66.5897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td>14.058324</td>\n",
       "      <td>108.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.335765</td>\n",
       "      <td>-64.8963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>West Bank and Gaza Strip</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>31.354676</td>\n",
       "      <td>34.3088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Cumulative no. of cases  \\\n",
       "0                     Algeria                       19   \n",
       "1         Antigua and Barbuda                       12   \n",
       "2                   Argentina                    14045   \n",
       "3                   Australia                    39519   \n",
       "4                     Austria                      173   \n",
       "..                        ...                      ...   \n",
       "143                 Venezuela                     1327   \n",
       "144                  Viet Nam                      822   \n",
       "145            Virgin Islands                        1   \n",
       "146  West Bank and Gaza Strip                      146   \n",
       "147                     Yemen                       50   \n",
       "\n",
       "     Cumulative no. of deaths        Lat      Lng  \n",
       "0                           0  28.033886  1.65963  \n",
       "1                           0  17.060816 -61.7964  \n",
       "2                         175 -38.416097 -63.6167  \n",
       "3                          39 -25.274398  133.775  \n",
       "4                           0  47.516231  14.5501  \n",
       "..                        ...        ...      ...  \n",
       "143                         0   6.423750 -66.5897  \n",
       "144                         0  14.058324  108.277  \n",
       "145                         0  18.335765 -64.8963  \n",
       "146                         0  31.354676  34.3088  \n",
       "147                         0  15.552727  48.5164  \n",
       "\n",
       "[135 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['Lat'].replace('',np.nan,inplace = True)\n",
    "grouped_df = grouped_df.dropna()\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "locations = grouped_df[[\"Lat\", \"Lng\"]]\n",
    "\n",
    "rating = grouped_df[\"Cumulative no. of cases\"]\n",
    "# Plot Heatmap\n",
    "fig = gmaps.figure()\n",
    "\n",
    "# Create heat layer\n",
    "heat_layer = gmaps.heatmap_layer(locations,weights=rating, \n",
    "                                 dissipating=False, max_intensity=100,\n",
    "                                 point_radius=1)\n",
    "\n",
    "# Add layer\n",
    "fig.add_layer(heat_layer)\n",
    "\n",
    "# Display figure\n",
    "fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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